Lecture 09: Data Structure Transformations. Geography 128 Analytical and Computer Cartography Spring 2007 Department of Geography University of California, Santa Barbara.
Analytical and Computer Cartography
Department of Geography
University of California, Santa Barbara
Geocoding stamps coordinate system, resolution and projection onto objects
Data usually in generic formats at first
Can save space, gain flexibility, decrease processing time
Suit demands of analysis and modeling
Suit demands of map symbolization (e.g. fonts)Why Transform Between Structures?
Simplicity -> clarity
Information will be lostGeneralization Transformations- Why Generalize?
John Krygier and Denis Wood, Making Maps:
a visual guide to map design for GIS
Douglas-PeuckerGeneralization Transformations - Line-to-Line Generalization
Douglas-Peucker line generalization
Trigonometric Functions (Fourier-based)Generalization Transformations - Line-to-Line Enhancement
Example: Convert census tract data to zip codes for marketing
Example: Convert crime data by police precinct to school district
May require dividing non-divisible measures, e.g population
Greatest common geographic units: Full overlap set for reassignmentGeneralization Transformations - Area-to-Area
Population at counties
Population at watersheds=?
Often via points and interpolation
Change cell size
Generate a new grid
Compute the intersect
Interpolate from neighboring cells
Problem of VIPsGeneralization Transformations Volume-to-Volume
Grid must relate to coordinates (extent, bounds, resolution, orientation)
Rasters can be square, rectangular, hexagonal.
Resample at minimum r/2Vector-to-Raster Transformations
Sample and convert to grid indices
Thin fat lines
Compute implicit inclusion (anti-alias)Vector-to-Raster Transformations (cnt.)- Algorithm
Morrison: Method-produced error
Error is inherent, can it be predicted, controlled or minimized?
XT = X\'
X\' T^-1 = X + EThe Role of Error